Intelligent Optimization in Power Electronics: Methods, Applications, and Practical Limits

Power electronic converters are being pushed toward higher power density and switching frequency, turning both design and operation into multi-objective, multi-physics optimization problems. While analytical rules and gradient-based methods remain essential, they often struggle with non-convex, mixed-integer trade-offs that include thermal behavior, Electromagnetic Interference/Electromagnetic Compatibility (EMI/EMC), and reliability constraints. This review surveys intelligent optimization approaches for power electronics across design-time, commissioning-time, and run-time horizons. We propose a deployment-oriented taxonomy of intelligent optimization approaches covering metaheuristics, surrogate-assisted and learning-guided design, constrained optimization via model predictive control, reinforcement learning-based supervisory policies, and hybrid physics-informed methods. For each family, we summarize typical tasks, computational and data requirements, robustness, interpretability, and validation maturity, highlighting where intelligent methods provide clear benefits and where classical approaches remain preferable. Reliability- and diagnostics-oriented optimization is discussed with emphasis on residual-based monitoring, stress-aware operation, and lifetime proxies. Practical adoption barriers—model–reality mismatch, data scarcity, real-time determinism, and certification—are synthesized into recurring design patterns that improve deployability. Finally, a conceptual cognitive design framework is proposed that couples virtual engineering, physics-informed surrogates, human-in-the-loop validation, and knowledge reuse in a closed-loop workflow, offering a structured perspective on how intelligent optimization may be integrated more reliably into industrial design practice.